Comment by blensor | Hacker News Reader

Comment by blensor | Hacker News Reader

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Simple answer: simplicity

Our world around us is 3D. A camera projects this information onto a 2D plane and throws away a lot of information in the process.

When we move in the world we need to know where obstacles are relative to us. We infer this from all the sensory input we receive and have a pretty good idea how far things are away from us because we have learned how size relations should be.

A computer could and can (to some degree) infer this information from regular 2D videos as well, because in the end most algorithms that do obstacle detection/avoidance have to extract positions in 3D to know exactly where those obstacles are.

A LIDAR (and other depth sensing technologies) provide a distance for each measurement (sometimes the distance is the only information but some sensors provide intensity, color, etc. as well).

So we immediately know the position in space of the measured point and thus can more or less directly extract obstacles in 3D space relative to the robot/car/drone without going the detour of "imagining" 3D information from the projected 2D image.

So it is

3D world -> 3D sensor -> 3D points -> 3D obstacles

versus

3D world -> 2D sensor(s) -> Extraction of depth -> 3D points -> 3D obstacles

It's like how we build cars with wheels instead of legs.

No reason to stick with our limitations.

Not so much limitation, as much as optimized for a different problem. Wheels are great for going fast on relatively level terrain, not for traversing any terrain it encounters.

Lidar is great for distance representations, but requires a highly consistent and exact timing source. Dual optical inputs allow us to approximate the same data through other methods that don't require nearly as exact timing.

> Lidar is great for distance representations, but requires a highly consistent and exact timing source.

Just a small FYI:

If you are doing time-of-flight measurement, then yes. But many (not all) LIDARs out there don't do time-of-flight, because to get the resolution wanted or needed, you need to have an oscillator (and sensors for the return pulse) capable of many, many gigahertz (it can get absurd quickly if you want anything below a resolution of around 10 CMs). This isn't easy or cheap (the oscillator actually is fairly easy and cheap - it's the return sensor that needs a very fast rise time that is expensive and difficult, though such devices do exist - some all solid-state, like flash LIDAR).

What many LIDAR sensors use (not sure about Velodyne) is actually using a kind of "doppler shift" of a laser modulated with a carrier wave. It still needs a high-speed multi-gigahertz oscillator to resolve small changes in distance, but everything else is fairly off-the-shelf and nothing real special. One downside of the method is that there is a discrepancy at farthest vs minimal distance measured due to ambiguity when comparing the received modulated beam to the outgoing modulated beam; you can't tell at such a point what the distance is (because both distances look the same).

Multi-frequency phase interference can be used to get sub-cm accuracy without needing ridiculous oscillators. The only problem is that you need to sample your target several times with different frequencies to remove the ambiguity.

Units like this:

https://www.amazon.com/Bosch-GLM-35-Measure-120-Feet/dp/B00V...

that go for $75 (or less) do this. They're super accurate, the only downside is that it takes a second to cycle through all the necessary frequencies.

Are you sure you mean doppler shift and not just phase shift due to the distance the light traveled? AFAIK the Time of Flight cameras like the ones from PMD use the phase shift with the obvious problem of the calculated distance being ambiguos beyond a certain distance due to the periodic signal.

But sensors like Velodyne, SICK and I think Hokuyo are pulsed and measure the actual time it takes for the pulse to travel from the emitter to the target and back to the detector.

Sure, it's the road network that really makes wheels work. But we built that instead of machines with legs.

Depending on your technology level in a few areas, a road network with wheeled vehicles is likely either much harder or much easier than legged vehicles of some sort. Tracked vehicles probably fall somewhere in between.

I winder, if we started heavily colonizing Mars 100 years from today, would we bother to build roads?

I guess that would depend on if there was some reason to try to have extensive industry on Mars or not.

At first people would anyway live by whatever resource they were using. If a deposit ran out they might choose to build a haul road over moving their entire settlement.

But then to save time, they start to just put their domed cities on huge caterpillar tracks. To keep from having to bury their city for radiation protection, they start adding anti-radiation armor. However, the laws on Mars break down after Earth civilization collapses, and these tracked cities start arming themselves with cannon.

Cue action music.

It's a sucky situation, but it makes for an awesome video game!

https://en.wikipedia.org/wiki/Chrome_Shelled_Regios

Since gravity there is only 0,38 of that on earth, flying might be more economical than driving, even for short distances.

Isn't the atmosphere much thinner as well? Depending on the mode of flight, that might offset some of the economy.

One thing LIDAR doesn't provide is immediate knowledge of velocity of a moving obstacle (like other cars). It can be used to infer this (over two or more measurements), but it can't get at that information directly.

For that matter, neither can a camera (or multiple cameras), but I just wanted to point it out, because it is something that can be useful to know in self-driving vehicles, and isn't often considered or mentioned.

What technology can get that information directly, considering velocity is always denominated by time?

FMCW LIDAR can capture velocity directly from Doppler Shift analysis.

Is there a sensor technology that does this more quickly? My naive understanding was that sonar and radar also infer velocity by comparing multiple passes (and light should be at least as fast as radar, shouldn't it?)

Active sonar and radar can get motion towards or away from the sensor in a single pass from the Doppler shift of the pulse.

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